class_docstr: A variational block based on Sriram et. al., "End-to-end variational networks for accelerated MRI reconstruction".
  It applies data consistency and refinement to the intermediate kspace and combines those results.
  
  Modified and adopted from: https://github.com/facebookresearch/fastMRI
  
  Args:
      refinement_model: the model used for refinement (typically a U-Net but can be any deep learning model
          that performs well when the input and output are in image domain (e.g., a convolutional network).
      spatial_dims: is 2 for 2D data and is 3 for 3D data
code: def __init__(
      self, refinement_model: nn.Module, spatial_dims: int = 2
  ):
      super().__init__()
      self.model = refinement_model
      self.spatial_dims = spatial_dims
      self.dc_weight = nn.Parameter(
          torch.ones(1)
      )  # learned scalar as the multiplier of the DC block
  
      buffer_shape = [
          1 for _ in range(spatial_dims + 3)
      ]  # 3 denotes the batch, channel, and real/complex dimensions
      self.register_buffer("zeros", torch.zeros(buffer_shape))
  
file_path: MONAI/monai/apps/reconstruction/networks/blocks/varnetblock.py
imports: from __future__ import annotations
  import torch
  import torch.nn
  from torch import Tensor
  from monai.apps.reconstruction.networks.nets.utils import sensitivity_map_expand, sensitivity_map_reduce
label_desc: Use image node_id 1 to create a new VarNetBlock object from inherited base classes: nn with example: obj = VarNetBlock(refinement_model, spatial_dims)